Onion Price and Quantity

Frame

library(prophet)

Loading required package: Rcpp

Acquire

str(pg.table)

'data.frame':   3784 obs. of  7 variables:
 $ Market              : chr  "ABOHAR(PB)" "ABOHAR(PB)" "ABOHAR(PB)" "ABOHAR(PB)" ...
 $ Month Name          : chr  "January" "January" "January" "February" ...
 $ Year                : chr  "2014" "2015" "2017" "2014" ...
 $ Arrival (q)         : int  440 1305 200 1115 1115 1300 920 670 1350 940 ...
 $ Price Minimum (Rs/q): chr  "1025" "1309" "750" "831" ...
 $ Price Maximum (Rs/q): chr  "1481" "1858" "1000" "1163" ...
 $ Modal Price (Rs/q)  : chr  "1256" "1613" "850" "983" ...

Refine

dim(df)

[1] 3783    7

str(df)

'data.frame':   3783 obs. of  7 variables:
 $ market  : chr  "ABOHAR(PB)" "ABOHAR(PB)" "ABOHAR(PB)" "ABOHAR(PB)" ...
 $ month   : chr  "January" "January" "January" "February" ...
 $ year    : num  2014 2015 2017 2014 2015 ...
 $ quantity: num  440 1305 200 1115 1115 ...
 $ priceMin: num  1025 1309 750 831 1200 ...
 $ priceMax: num  1481 1858 1000 1163 1946 ...
 $ priceMod: num  1256 1613 850 983 1688 ...

str(df)

'data.frame':   3783 obs. of  10 variables:
 $ market  : chr  "ABOHAR(PB)" "ABOHAR(PB)" "ABOHAR(PB)" "ABOHAR(PB)" ...
 $ month   : chr  "January" "January" "January" "February" ...
 $ year    : num  2014 2015 2017 2014 2015 ...
 $ quantity: num  440 1305 200 1115 1115 ...
 $ priceMin: num  1025 1309 750 831 1200 ...
 $ priceMax: num  1481 1858 1000 1163 1946 ...
 $ priceMod: num  1256 1613 850 983 1688 ...
 $ city    : chr  "ABOHAR" "ABOHAR" "ABOHAR" "ABOHAR" ...
 $ state   : chr  "PB" "PB" "PB" "PB" ...
 $ date    : Date, format: "2014-01-01" "2015-01-01" "2017-01-01" "2014-02-01" ...

Transform

Split-Apply-Combine

Explore

ggplotly(g)

We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`

Model

dfBang <- df %>%
  filter(city == "BANGALORE") %>%
  select(date, priceMod) %>%
  arrange(date)
ggplot(dfBang) + aes(date, priceMod) + geom_line()

prophet_plot_components(m, forecast)

Insight

uniqcity <- unique(dfCity$city)
geo <- geocode(uniqcity)

Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=BANGALORE&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=DELHI&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=LASALGAON&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=MAHUVA&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=MUMBAI&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=NEWASA&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=PIMPALGAON&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=PUNE&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=SOLAPUR&sensor=false

dfGeo <- bind_cols(df2016City, geo)
dfGeo

ggplot(dfGeo) + aes(lon, lat, size=quantity_year/1000) + geom_point() + coord_map()

map <- get_map("India", zoom = 5)

Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=India&zoom=5&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=India&sensor=false

ggmap(map)

map1 <- get_map("India", maptype = "watercolor", source = "stamen", zoom = 5)

Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=India&zoom=5&size=640x640&scale=2&maptype=terrain&sensor=false
Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=India&sensor=false
Map from URL : http://tile.stamen.com/watercolor/5/21/12.jpg
Map from URL : http://tile.stamen.com/watercolor/5/22/12.jpg
Map from URL : http://tile.stamen.com/watercolor/5/23/12.jpg
Map from URL : http://tile.stamen.com/watercolor/5/24/12.jpg
Map from URL : http://tile.stamen.com/watercolor/5/21/13.jpg
Map from URL : http://tile.stamen.com/watercolor/5/22/13.jpg
Map from URL : http://tile.stamen.com/watercolor/5/23/13.jpg
Map from URL : http://tile.stamen.com/watercolor/5/24/13.jpg
Map from URL : http://tile.stamen.com/watercolor/5/21/14.jpg
Map from URL : http://tile.stamen.com/watercolor/5/22/14.jpg
Map from URL : http://tile.stamen.com/watercolor/5/23/14.jpg
Map from URL : http://tile.stamen.com/watercolor/5/24/14.jpg
Map from URL : http://tile.stamen.com/watercolor/5/21/15.jpg
Map from URL : http://tile.stamen.com/watercolor/5/22/15.jpg
Map from URL : http://tile.stamen.com/watercolor/5/23/15.jpg
Map from URL : http://tile.stamen.com/watercolor/5/24/15.jpg

ggmap(map1)

ggmap(map1) + geom_point(data = dfGeo,aes(lon,lat,size=quantity_year/1000,color=city))